FEDERAL RESERVE BANK OF SAN FRANCISCO
WORKING PAPER SERIES
Uncertainty Shocks are Aggregate Demand Shocks
Sylvain Leduc
Federal Reserve Bank of San Francisco
Zheng Liu
Federal Reserve Bank of San Francisco
May 2015
Working Paper 2012-10
http://www.frbsf.org/publications/economics/papers/2012/wp12-10bk.pdf
Suggested citation:
Leduc, Sylvain and Zheng Liu. 2015. “Uncertainty Shocks are Aggregate Demand
Shocks.” Federal Reserve Bank of San Francisco Working Paper 2012-10.
http://www.frbsf.org/economic-research/publications/working-papers/wp12-10bk.pdf
The views in this paper are solely the responsibility of the authors and should not be
interpreted as reflecting the views of the Federal Reserve Bank of San Francisco or the
Board of Governors of the Federal Reserve System.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
SYLVAIN LEDUC AND ZHENG LIU
Abstract. We show that to capture the empirical effects of uncertainty on the unemployment rate, it is crucial to study the interactions between search frictions and nominal
rigidities. Our argument is guided by empirical evidence showing that an increase in uncertainty leads to a large increase in unemployment and a significant decline in inflation,
suggesting that uncertainty partly operates via an aggregate demand channel. To understand the mechanism through which uncertainty generates these macroeconomic effects, we
incorporate search frictions and nominal rigidities in a DSGE model. We show that an
option-value channel that arises from search frictions interacts with a demand channel that
arises from nominal rigidities, and such interactions magnify the effects of uncertainty to
generate roughly 60 percent of the observed increase in unemployment following an uncertainty shock.
I. Introduction
Measures of uncertainty surged in the Great Recession of 2008-2009 and remained elevated during most of the recovery period. Some have argued that increases in uncertainty
contributed to the deep recession and the slow recovery, with a persistently high unemployment rate. In this paper, we present a DSGE framework with search frictions and nominal
rigidities to illustrate a new transmission mechanism through which uncertainty can produce large macroeconomic effects. This mechanism works through an interaction between
Date: May 11, 2015.
Key words and phrases. Uncertainty, aggregate demand, labor search frictions, option-value channel,
unemployment, inflation, survey data.
JEL classification: E21, E27, E32.
Leduc: Federal Reserve Bank of San Francisco; Email: Sylvain.Leduc@sf.frb.org. Liu: Federal Reserve
Bank of San Francisco; Email: Zheng.Liu@sf.frb.org. We thank the editors Ricardo Reis and Nir Jaimovich
and an anonymous referee for critical comments that have helped improve the quality of the paper. We also
thank Susanto Basu, Nick Bloom, Toni Braun, Larry Christiano, Mary Daly, John Fernald, Jesús FernándezVillaverde, Simon Gilchrist, Federico Ravenna, Juan F. Rubio-Ramı́rez, John Williams, Tao Zha and seminar
participants at the Federal Reserve Banks of Atlanta and San Francisco, the Bank of Canada, the Canadian
Macro Study Group, the 2012 NBER Productivity and Macroeconomics meeting, the 2012 NBER Workshop
on Methods and Applications for Dynamic Stochastic General Equilibrium Models, the 2013 SED meeting
in Seoul, and the 2013 Summer Workshop of the Stanford Institute for Theoretical Economics. We are
especially grateful to Tao Zha for providing us with his computer code for estimating Bayesian VAR models.
The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal
Reserve Bank of San Francisco or the Federal Reserve System.
1
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
2
an option-value channel that arises from labor search frictions and an aggregate-demand
channel associated with nominal rigidities.
Our theoretical framework is guided by empirical evidence about the joint dynamics of
unemployment and inflation following an uncertainty shock. We show that, in a small-scale
Bayesian vector-autoregression (BVAR) model, an increase in uncertainty raises unemployment and lowers inflation, suggesting that the macroeconomic effects of uncertainty operate
partly through an aggregate-demand channel. This empirical pattern is robust to the use of
alternative measures of uncertainty, including standard measures such as the CBOE Volatility Index (VIX) and a novel measure of consumers’ perceived uncertainty constructed using
data from the Thomson Reuters/University of Michigan Surveys of Consumers. Despite the
weak correlation between the raw time series of the VIX and consumer uncertainty measure,
the BVAR model consistently predicts that uncertainty shocks act like a negative aggregate
demand shock, raising unemployment and lowering inflation, independent of the particular
measure of uncertainty used. Furthermore, the BVAR evidence suggests that uncertainty
shocks lead to large increases in the unemployment rate. The large real effect of uncertainty
shocks presents a challenge for standard DSGE models (e.g., Born and Pfeifer (2014)).
We use our theoretical framework to examine the mechanism through which uncertainty
shocks can generate the large increase in unemployment and the fall in inflation observed
empirically. Consistent with our VAR evidence, the DSGE model predicts that a rise in
uncertainty raises the unemployment rate and lowers the inflation rate. More importantly,
our model suggests that the interactions between search frictions and nominal rigidities are
the key to obtaining the observed large increase in unemployment following an uncertainty
shock.
I.1. The key mechanism. First, nominal rigidities help amplify the effect of uncertainty
shocks on the unemployment rate through declines in aggregate demand, as in the standard
DSGE model without search frictions (Fernández-Villaverde et al., 2013; Basu and Bundick,
2011). In our model with search frictions, the decline in aggregate demand reduces the value
of a new match so that firms post fewer job vacancies, pushing the unemployment rate up.
As more searching workers fail to find a job match, household incomes decline further. This
leads to an even greater fall in aggregate demand, which magnifies the effects of uncertainty
shocks. In addition, with sticky prices, inflation falls as aggregate demand declines, in line
with our evidence.
Second, search frictions provide an additional mechanism for uncertainty shocks to generate large increases in unemployment via an option-value channel. With search frictions, a
job match represents a long-term employment relationship that is irreversible. When times
are uncertain, the option value of waiting increases and the match value declines. Firms
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
3
respond by reducing hiring. This option-value effect in our model with search frictions arises
for a similar reason as in the literature of irreversible investment decisions under uncertainty
(Bernanke, 1983; Bloom, 2009; Bloom et al., 2012).
Indeed, in our model with search frictions, uncertainty shocks can be contractionary even
with flexible prices, in contrast to the real business cycles (RBC) model with a spot labor
market. A contraction occurs as long as the option-value channel dominates the effect
of precautionary savings, which lowers interest rates and boosts the present value of a job
match. When prices are sticky, an increase in uncertainty also leads to a decline in aggregate
demand (the demand channel), which reinforces the option-value channel and generates an
increase in unemployment that is roughly 60 percent of that observed in the data. Search
frictions also amplify the decline in inflation, although the magnitude of the fall in prices
remains notably smaller than what we see in the data.
We then examine the relative importance of these two channels through the lens of our
model. Absent significant search frictions, the demand channel alone accounts for at most 16
percent of the observed increase in the unemployment rate following an uncertainty shock.
This relatively small impact of uncertainty on real economic activity is in line with the recent
findings of Born and Pfeifer (2014) using a standard DSGE model without search frictions.
Similarly, absent nominal rigidities, the option-value channel alone accounts for a small
fraction of the observed increase in the unemployment rate following an uncertainty shock.
This finding is consistent with Schaal (2012), who studies a search model with flexible prices.
He obtains sizable effects from idiosyncratic volatility shocks—his measure of uncertainty—
but partly by assuming that these volatility shocks are negatively correlated with the level of
aggregate productivity. Instead, when the option-value channel and the demand channel are
simultaneously operating, as in our model, they interact to amplify the effects of uncertainty
shocks and generate unemployment responses that are broadly in line with the data.
I.2. Relation to the literature. Our work adds to the recent burgeoning literature on the
macroeconomic effects of uncertainty. On the empirical side, we document a new stylized
fact that uncertainty acts like a decline in aggregate demand that raises unemployment and
lower inflation. We show that this result holds when we use the standard VIX to measure
uncertainty or when we use a new measure of consumers’ perceived uncertainty constructed
from survey data. Our empirical approach thus complements those in the literature, where
uncertainty is measured using cross-sectional dispersions of earnings or productivity at the
firm or industry levels (Bloom, 2009; Bloom et al., 2012), the conditional variance of the
unforecastable component in statistical models (Jurado et al., 2013; Scotti, 2012), forecast
disagreements (Bloom, 2009; Bachmann et al., 2013), or the volatility of fiscal instruments
estimated under time-varying volatilities (Fernández-Villaverde et al., 2013).
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
4
On the theory side, we show that an uncertainty shock can generate the observed demandlike macroeconomic effects through interactions between an option-value channel stemming
from search frictions and an aggregate demand channel associated with nominal rigidities.
Our model complements the recent theoretical literature on the macroeconomic effects of
uncertainty. This strand of literature is too vast to enumerate.1 However, to our knowledge,
our emphasis on the interactions between the option-value channel and the aggregate-demand
channel for the transmission of uncertainty shocks is new to the literature.
II. Uncertainty, unemployment and inflation: Empirical evidence
In this section, we examine the macroeconomic effects of uncertainty shocks in the data.
We consider two alternative measures of uncertainty: The VIX commonly used in the literature (Bloom, 2009), and a new measure of consumers’ perceived uncertainty (or consumer uncertainty) constructed from the Thomson Reuters/University of Michigan Surveys
of Consumers. We show that, although the time series behaviors of these two measures of
uncertainty are quite different, the macroeconomic effects of uncertainty shocks based on the
two different measures are remarkably similar.
II.1. The measures of uncertainty. Our new measure of uncertainty is constructed based
on the Michigan Survey. Since January 1978, the Michigan Survey has conducted monthly
interviews of about 500 households throughout the United States. One survey question asks:
“Speaking now of the automobile market–do you think the next 12 months or so will be
a good time or a bad time to buy a vehicle, such as a car, pickup, van or sport utility
vehicle?” A follow-up question asks: “Why do you say so?” The survey tallies the fraction
of respondents who report that “uncertain future” is a reason why it will be a bad time to
buy cars or other durable goods over the next 12 months. The series is weighted by age,
income, region, and sex and is nationally representative.
Figure 1 shows the time-series plots of consumers’ perceived uncertainty (concerning vehicle purchases) along with the VIX.2 The main similarity between the two measures of
uncertainty is that they are both countercyclical. They rise in recessions and fall in expansions. However, the two series appear otherwise very different. One notable difference
is in 1997-98. The VIX surged following the East Asian financial crisis and the Russian
debt crisis, but consumer uncertainty remained at historically low levels. Another notable
1
A partial list includes Bloom (2009), Gilchrist et al. (2010), Arellano et al. (2011), Basu and Bundick
(2011), Ilut and Schneider (2011), Fernández-Villaverde et al. (2013), Bloom et al. (2012), Gourio (2012),
Bidder and Smith (2012), and Born and Pfeifer (2014). See Bloom (2014) for a survey.
2
The VIX constructed by the Chicago Board of Exchange (CBOE) starts in January 1990. We extend
the series back to January 1986 by using the CBOE’s VXO for the pre-1990 period.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
5
difference is in late 2012, when the U.S. economy faced the possibility of a “fiscal cliff”
that could potentially trigger large tax increases and government spending cuts if Congress
and the White House failed to reach an agreement about deficit reductions. In that period,
consumer uncertainty remained elevated but the VIX was very low.3 Anecdotal evidence
aside, the two measures of uncertainty are only weakly correlated, with a sample correlation
of about 0.24 during the period from January 1986 (the beginning of the VIX/VXO series)
and October 2013.
II.2. VAR evidence. Despite the apparent differences in the raw time series of our consumer uncertainty measure and the VIX, we find that the macroeconomic effects of uncertainty shocks in an estimated BVAR model are remarkably similar across the two measures
of uncertainty.
The baseline BVAR model contains four time-series variables: a measure of uncertainty
(consumer uncertainty or VIX), the unemployment rate, the inflation rate measured as yearover-year changes in the consumer price index (CPI), and the three-month Treasury bills
rate. When we use the consumer uncertainty measure, the sample covers the period from
January 1978 to October 2013. When we use the VIX, the sample ranges from January 1986
to October 2013. We adopt a flat prior for estimating the BVAR model.4
An advantage of our consumer uncertainty measure relative to the VIX is that we are able
to exploit the timing of the survey interviews relative to the timing of macroeconomic data
releases to identify the uncertainty shock. In the Michigan survey, telephone interviews are
conducted throughout the month, with most interviews concentrated in the middle of each
month, and preliminary results released shortly thereafter. The final results are typically
released by the end of the month. When answering questions, survey participants have
information about the previous month’s unemployment, inflation, and interest rates, but
they do not have (complete) information about the current-month macroeconomic conditions
because the macroeconomic data have not yet been made public. Hence, our identification
strategy uses the fact that, when answering questions at time t about their expectations of
the future, the information set on which survey participants condition their answers will not
include, by construction, the time t realizations of the unemployment rate and the other
variables in our BVAR model.
Thus, we place the consumer uncertainty measure as the first variable in the BVAR model.
This Cholesky ordering implies that uncertainty does not respond to macroeconomic shocks
3
A possible explanation is that the VIX focuses on short-term uncertainty because it is a weighted average
of 30-day-ahead option prices.
4
Our results are robust to using Bayesian priors proposed by Sims and Zha (1998), which may help
improve the estimation of error bands in short time-series samples of data.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
6
in the impact period, but unemployment, inflation, and the nominal interest rate are allowed
to respond to an uncertainty shock. In subsequent periods, however, uncertainty responds
to all shocks through its relation to the lags of the macroeconomic variables as specified in
the BVAR model. Our Cholesky identification strategy here is similar to that in Leduc et al.
(2007), Auerbach and Gorodnichenko (2012), and Leduc and Sill (forthcoming).5
Figure 2 presents the impulse responses in the BVAR model, in which consumer uncertainty is ordered first. For each variable, the solid line denotes the median estimate of the
impulse response and the dashed lines represent the range of the 90-percent confidence band
around the point estimates. The figure shows that an unexpected increase in uncertainty
leads to a persistent increase in the unemployment rate. The increase in unemployment remains significant at the 90-percent level for about three years, with the peak effect occurring
about 18 months from the impact period.
Heightened uncertainty also leads to a persistent decline in the inflation rate, with the
peak effect occurring roughly 20 months from the impact period. The decline in inflation
becomes significant at the 90-percent level in about eight months and remains significant for
about two years. The rise in unemployment and the fall in inflation suggest that uncertainty
operates through an aggregate demand channel that reduces both economic activity and
prices.
Although the timing of the survey relative to macroeconomic data releases suggests that
survey respondents do not possess complete information about the current-month macroeconomic data at the time of the interviews (which forms the basis of our Cholesky identification assumption in the benchmark BVAR model), they may observe other, possibly
higher-frequency indicators that provide information about the time t realizations of the
variables in the BVAR model. To examine the sensitivity of our results to the Cholesky ordering of variables, we estimate a BVAR model with consumer uncertainty ordered last. This
relatively conservative identification assumption implies that the measure of uncertainty is
allowed to respond to all contemporaneous macroeconomic shocks.
Figure 3 presents the impulse responses in the BVAR model with this alternative ordering.
The responses of the three macroeconomic variables to an uncertainty shock look remarkably similar to those in the benchmark BVAR with uncertainty ordered first. Under each
identification strategy, a positive uncertainty shock acts like a negative aggregate demand
shock that leads to persistent increases in unemployment and persistent declines in inflation,
5Bachmann
and Moscarini (2011) argue that bad first-moment shocks can raise cross-sectional disper-
sions and time-series volatility of macroeconomic variables. In this sense, changes in measured uncertainty
could reflect endogenous responses of macroeconomic variables to first-moment shocks. Our empirical approach allows measured uncertainty to react to macroeconomic shocks, but it also presumes that measured
uncertainty contains some exogenous component.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
7
and the responses of both macroeconomic variables are statistically significant at the 90percent level. Under each identification, monetary policy reacts to the recessionary effects
of uncertainty by lowering the nominal interest rate.
The aggregate demand effects of uncertainty are not an artifact of our measure of consumer
uncertainty. They are also present when we replace the consumer uncertainty measure with
the VIX, as Figure 4 shows. In particular, following a shock to uncertainty (measured by
the VIX index), unemployment rises significantly for about two years, inflation declines significantly for about 15 months, and monetary policy accommodates by lowering the nominal
interest rate.
II.3. Robustness of VAR evidence. We now show that the aggregate demand effects of
uncertainty are robust when we replace the short-term nominal interest rate with a longterm interest rate to capture potential effects of unconventional monetary policy when the
short-term rates are constrained by the zero lower bound. We also show that the aggregate
demand effects of uncertainty do not reflect the responses of macroeconomic variables to
changes in consumer confidence. We focus on the survey-based measure of uncertainty. The
results are similar when we use the VIX instead.6
II.3.1. Monetary policy and the zero lower bound. Our sample covers the post-2008 period,
during which U.S. monetary policy has been constrained by the zero lower bound (ZLB)
on the short-term nominal interest rate. The inability of monetary policy to accommodate
the negative effects of uncertainty when the ZLB is binding implies that our BVAR model
could be misspecified. In reality, however, the Fed used unconventional monetary tools after
the short-term nominal interest rate reached the zero lower bound. In particular, the Fed
has conducted three rounds of large-scale asset purchases and provided systematic forward
guidance about the future path of monetary policy.
To examine the importance of this issue for our results, we estimate a BVAR model with
an alternative indicator of monetary policy. In particular, we use the two-year Treasury
bond yields as an indicator of monetary policy instead of the three-month Treasury bills
rate.7 Unlike the three-month Treasury bills rate, the two-year Treasury yields did not reach
the zero lower bound. More importantly, the use of this longer-term interest rate helps
capture the effects of unconventional monetary policy, which is designed to lower yields on
6To
conserve space, we relegate these empirical results to an online appendix available at
http://www.frbsf.org/economic-research/publications/working-papers/2012/
wp12-10bk_appendix.pdf.
7
Swanson and Williams (2014) argue that the Federal Reserve’s forward guidance policy typically attempts
to influence the two-year Treasury bond yields. Gertler and Karadi (2013) also argue for the use of a longterm interest rate as an indicator of monetary policy in a VAR.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
8
long-term securities. Thus, we examine a BVAR model that is identical to the baseline fourvariable BVAR model, except that the short-term nominal interest rate series is replaced by
a long-term interest rate series.
As in our baseline model, we find that a shock that raises uncertainty also raises the
unemployment rate and lowers both the inflation rate and the two-year Treasury yields.
These effects are persistent and statistically significant at the 90-percent level.
II.3.2. Uncertain future or bad economic times? As shown in Figure 1, consumer uncertainty
rises in recessions and falls in booms. A priori, it is possible that consumer uncertainty from
the Michigan Survey may reflect the respondents’ perceptions of bad economic times rather
than an uncertain future.
To assess the extent to which consumer uncertainty might reflect their perceptions of bad
economic times, we examine how much the macroeconomic effects of shocks to consumer
uncertainty reflect the responses to changes in other indicators of economic conditions, such
as consumer confidence. For this purpose, we follow an approach similar to Baker et al. (2011)
and estimate a five-variable BVAR model that includes a consumer sentiment index as an
additional variable to control for potential effects from movements in consumer confidence.
We find that the macroeconomic effects of uncertainty shocks are qualitatively similar to
those estimated from the benchmark BVAR. A shock to uncertainty raises the unemployment
rate and lowers consumer sentiment, the inflation rate, and the short-term nominal interest
rate. The responses of the macroeconomic variables are statistically significant at the 90
percent level. The results are similar when we replace the consumer sentiment for current
economic conditions with that for future expectations. Thus, the macroeconomic effects of
uncertainty shocks do not seem to reflect responses of macroeconomic variables to changes
in consumer confidence.
III. Uncertainty shocks in a DSGE model with search frictions
In this section, we examine the channels that transmit uncertainty shocks to the macroeconomy in a DSGE model with sticky prices and labor market search frictions. We show
that uncertainty shocks in the DSGE model act like an aggregate demand shock that raises
unemployment, lowers inflation, and, through the Taylor rule, lowers the nominal interest
rate, just as we have seen in the data. We further show that search frictions in the labor
market and sticky prices in the goods market are both important for amplifying the effects
of uncertainty shocks in the model.
The economy is populated by a continuum of infinitely lived and identical households with
a unit measure. The representative household consists of a continuum of worker members.
The household owns a continuum of firms, each of which uses one worker to produce an
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
9
intermediate good. In each period, a fraction of the workers are unemployed and they
search for jobs. Firms post vacancies at a fixed cost. The number of successful matches are
produced with a matching technology that transforms searching workers and vacancies into
an employment relation. Real wages are determined by Nash bargaining between a searching
worker and a hiring firm.
The household consumes a basket of differentiated retail goods, each of which is transformed from the homogeneous intermediate good using a constant-returns technology. Retailers face a perfectly competitive input market (where they purchase the intermediate
good) and a monopolistically competitive product market. Each retailer sets a price for its
differentiated product, with price adjustments subject to a quadratic cost in the spirit of
Rotemberg (1982).
The government finances its spending and transfer payments to unemployed workers by
distortionary taxes on firm profits. Monetary policy is described by the Taylor rule, under
which the nominal interest rate responds to deviations of inflation from a target and of
output from its potential.
III.1. The households. There is a continuum of infinitely lived and identical households
with a unit measure. The representative household consumes a basket of retail goods. The
utility function is given by
E
∞
X
β t [ln(Ct − hCt−1 ) − χNt ] ,
(1)
t=0
where E [·] is an expectation operator, Ct denotes consumption, and Nt denotes the fraction
of household members who are employed. The parameter β ∈ (0, 1) denotes the subjective
discount factor, h measures habit persistence, and χ captures the disutility from working.
The household chooses consumption Ct and saving Bt to maximize the utility function in
(1) subject to the sequence of budget constraints
Ct +
Bt
Bt−1
=
+ wt Nt + φ (1 − Nt ) + dt − Tt ,
Pt Rt
Pt
∀t ≥ 0,
(2)
where Pt denotes the price level, Bt denotes holdings of a nominal risk-free bond, Rt denotes
the nominal interest rate, wt denotes the real wage rate, φ denotes an unemployment benefit,
dt denotes profit income from ownership of intermediate goods producers and of retailers,
and Tt denotes a lump-sum tax paid to the government.
Optimal bond-holding decisions are described by the intertemporal Euler equation
1 = Et β
Λt+1 Rt
,
Λt πt+1
(3)
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
where πt ≡
Pt
Pt−1
10
denotes the inflation rate, and Λt denotes the marginal utility of consumption
given by
Λt =
1
βh
− Et
.
Ct − hCt−1
Ct+1 − hCt
(4)
III.2. The aggregation sector. Denote by Yt the final consumption good, which is a basket
of differentiated retail goods. Denote by Yt (j) a type j retail good for j ∈ [0, 1]. We assume
that
1
Z
Yt =
Yt (j)
η−1
η
η
η−1
,
(5)
0
where η > 1 is the elasticity of substitution between differentiated products.
Expenditure minimizing implies that demand for a type j retail good is inversely related
to the relative price, with the demand schedule given by
−η
Pt (j)
d
Yt (j) =
Yt ,
Pt
(6)
where Ytd (j) and Pt (j) denote the demand for and the price of a retail good of type j,
respectively. Zero-profit in the aggregate sector implies that the price index Pt is related to
the individual prices Pt (j) through the relation
Z 1
1−η
1
Pt (j) 1−η
Pt =
.
(7)
0
III.3. The retail goods producers. There is a continuum of retailers, each producing a
differentiated product using a homogeneous intermediate good as input. The production
function of a retail good of type j ∈ [0, 1] is given by
Yt (j) = Xt (j),
(8)
where Xt (j) is the input of intermediate goods used by retailer j and Yt (j) is the output. The
retail goods producers are price takers in the input market and monopolistic competitors in
the product markets, where they set prices for their products, taking as given the demand
schedule in equation (6) and the price index in equation (7).
Price adjustments are subject to the quadratic cost
2
Pt (j)
Ωp
− 1 Yt ,
2 πPt−1 (j)
(9)
where the parameter Ωp ≥ 0 measures the cost of price adjustments and π denotes the
steady-state inflation rate. Price adjustment costs are in units of aggregate output.
A retail firm that produces good j solves the profit-maximizing problem
"
#
2
∞
i
X
β Λt+i
Pt+i (j)
Ωp
Pt+i (j)
d
maxPt (j) Et
− qt+i Yt+i
(j) −
− 1 Yt+i ,
Λt
Pt+i
2 πPt+i−1 (j)
i=0
(10)
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
11
where qt denotes the relative price of intermediate goods. The optimal price-setting decision
implies that, in a symmetric equilibrium with Pt (j) = Pt for all j, we have
η − 1 Ωp πt πt
βΛt+1 Yt+1 πt+1 πt+1
qt =
+
− 1 − Et
−1 .
η
η π π
Λt Yt π
π
(11)
Absent price adjustment costs (i.e., Ωp = 0), the optimal pricing rule implies that real
marginal cost qt equals the inverse of the steady-state markup.
III.4. The Labor Market. In the beginning of period t, there are ut unemployed workers
searching for jobs and there are vt vacancies posted by firms. The matching technology is
described by the Cobb-Douglas function
mt = µuαt vt1−α ,
(12)
where mt denotes the number of successful matches and the parameter α ∈ (0, 1) denotes the
elasticity of job matches with respect to the number of searching workers. The parameter µ
scales the matching efficiency.
The probability that an open vacancy is matched with a searching worker (i.e., the job
filling rate) is given by
mt
.
(13)
vt
The probability that an unemployed and searching worker is matched with an open vacancy
qtv =
(i.e., the job finding rate) is given by
qtu =
mt
.
ut
(14)
In the beginning of period t, there are Nt−1 workers. A fraction ρ of these workers lose
their jobs. Thus, the number of workers who survive the job separation is (1 − ρ)Nt−1 . At
the same time, mt new matches are formed. Following the timing assumption in Blanchard
and Galı́ (2010), we assume that new hires start working in the period they are hired. Thus,
aggregate employment in period t evolves according to
Nt = (1 − ρ) Nt−1 + mt .
(15)
With a fraction ρ of employed workers separated from their jobs, the number of unemployed
workers searching for jobs in period t is given by
ut = 1 − (1 − ρ)Nt−1 .
(16)
Following Blanchard and Galı́ (2010), we assume full participation and define the unemployment rate as the fraction of the population who are left without a job after hiring takes place
in period t. Thus, the unemployment rate is given by
Ut = ut − mt = 1 − Nt .
(17)
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
12
III.5. The firms (intermediate good producers). A firm can produce only if it successfully hires a worker. The production function for a firm with one worker is given by
xt = Z t ,
where xt denotes output. The term Zt denotes an aggregate technology shock, which follows
the stationary stochastic process
ln Zt = ρz ln Zt−1 + σzt εzt .
(18)
The parameter ρz ∈ (−1, 1) measures the persistence of the technology shock. The term εzt
is an i.i.d. innovation to the technology shock and is a standard normal process. The term
σzt is a time-varying standard deviation of the innovation, which we interpret as a technology
uncertainty shock. We assume that the uncertainty shock follows the stationary stochastic
process
ln σzt = (1 − ρσz ) ln σz + ρσz ln σz,t−1 + σσz εσz ,t ,
(19)
where the parameter ρσz ∈ (−1, 1) measures the persistence of the uncertainty shock, the
term εσz ,t is an i.i.d. standard normal process, and the parameter σσz > 0 is the standard
deviation of the innovation to technology uncertainty.
If a firm finds a match, it obtains a flow profit in the current period after paying the worker.
In the next period, if the match survives (with probability 1 − ρ), the firm continues; if the
match breaks down (with probability ρ), the firm posts a new job vacancy at a fixed cost κ,
with the value Vt+1 . The value of a firm with a match (denoted by JtF ) is therefore given by
the Bellman equation
JtF = qt Zt − wt + Et
βΛt+1 F
(1 − ρ) Jt+1
+ ρVt+1 .
Λt
(20)
If the firm posts a new vacancy in period t, it costs κ units of final goods. The vacancy
can be filled with probability qtv , in which case the firm obtains the value of the match.
Otherwise, the vacancy remains unfilled and the firm goes into the next period with the
value Vt+1 . Thus, the value of an open vacancy is given by
Vt = −κ + qtv JtF + Et
βΛt+1
(1 − qtv ) Vt+1 .
Λt
Free entry implies that Vt = 0, so that
κ
= JtF .
qtv
(21)
This relation describes the optimal job creation decisions. The benefit of creating a new job
is the match value JtF . The expected cost of creating a new job is the flow cost of posting a
vacancy κ multiplied by the expected duration of an unfilled vacancy 1/qtv .
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
13
III.6. Workers’ value functions. If a worker is employed, he obtains wage income but
suffers a utility cost of working. In period t+1, the match is separated with probability ρ and
u
. Thus, with probability ρ(1−
the separated worker can find a new match with probability qt+1
u
), a separated worker fails to find a new job in period t + 1 and enters the unemployment
qt+1
pool. Otherwise, the worker continues to be employed. The (marginal) value of an employed
worker (denoted by JtW ) therefore satisfies the Bellman equation
JtW = wt −
W
χ
βΛt+1 u
u
U
+ Et
1 − ρ(1 − qt+1
) Jt+1
+ ρ(1 − qt+1
)Jt+1
,
Λt
Λt
(22)
where JtU denotes the value of an unemployed worker. An unemployed worker obtains the
u
flow unemployment benefit φ and can find a new job in period t + 1 with probability qt+1
.
Thus, the value of an unemployed worker satisfies the Bellman equation
βΛt+1 u W
u
U
JtU = φ + Et
qt+1 Jt+1 + (1 − qt+1
)Jt+1
.
Λt
(23)
III.7. The Nash bargaining wage. Firms and workers bargain over wages. The Nash
bargaining problem is given by
max
wt
JtW − JtU
b
JtF
1−b
,
(24)
where b ∈ (0, 1) represents the bargaining weight for workers.
Define the total surplus as
St = JtF + JtW − JtU .
(25)
Then the bargaining solution is given by
JtF = (1 − b) St ,
JtW − JtU = bSt .
(26)
It then follows from equations (22) and (23) that
bSt = wtN − φ −
χ
βΛt+1 u
(1 − ρ) 1 − qt+1
bSt+1 .
+ Et
Λt
Λt
(27)
Given the bargaining surplus St , which itself is proportional to the match value JtF , this last
equation determines the Nash bargaining wage wtN .
If the equilibrium real wage equals the Nash bargaining wage, then we can obtain an
explicit expression for the Nash bargaining wage. Specifically, we use equations (21), (26),
and (27) and impose wt = wtN to obtain
βΛt+1 κvt+1
χ
N
wt = (1 − b)
+ φ + b qt Zt + β(1 − ρ)Et
.
Λt
Λt ut+1
(28)
In this case, the Nash bargaining wage is a weighted average of the worker’s reservation value
and the firm’s productive value of a job match. By forming a match, the worker incurs a
utility cost of working and forgoes unemployment benefits. By employing a worker, the firm
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
14
receives the marginal product from labor in the current period and saves the vacancy cost
from the next period.
III.8. Wage Rigidity. In general, however, the equilibrium real wage may be different from
the Nash bargaining solution. Hall (2005) points out that real wage rigidity is important to
generate empirically reasonable volatilities of vacancies and unemployment.
There are several ways to formalize real wage rigidity (Hall, 2005; Hall and Milgrom, 2008;
Gertler and Trigari, 2009; Blanchard and Galı́, 2010). We follow the literature and assume
that
γ
wt = wt−1
(wtN )1−γ ,
(29)
where γ ∈ (0, 1) represents the degree of real wage rigidity.8
III.9. Government policy. The government finances transfer payments for unemployment
benefits through lumpsum taxes. We assume that the government balances the budget in
each period so that
φ(1 − Nt ) = Tt .
The monetary authority follows the Taylor rule
φπ Y φy
t
∗ πt
Rt = rπ
,
π∗
Y
(30)
(31)
where the parameter φπ determines the aggressiveness of monetary policy against deviations
of inflation from the target π ∗ and φy determines the extent to which monetary policy
accommodates output fluctuations. The parameter r denotes the steady-state real interest
rate (i.e., r =
R
).
π
III.10. Search equilibrium. In a search equilibrium, the markets for bonds, final consumption goods, and intermediate goods all clear.
Since the aggregate supply of the nominal bond is zero, the bond market-clearing condition
implies that
Bt = 0.
Goods market clearing implies the aggregate resource constraint
2
Ω p πt
Ct + κvt +
− 1 Yt = Yt ,
2 π
where Yt denotes aggregate output of final goods.
(32)
(33)
Intermediate goods market clearing implies that
Yt = Zt Nt .
8We
(34)
have examined other wage rules such as those in Blanchard and Galı́ (2010) and we find that our
results do not depend on the particular form of the wage rule.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
15
IV. Economic implications of the DSGE model
To examine the macroeconomic effects of uncertainty shocks in our DSGE model, we
calibrate the model parameters and simulate the model to examine impulse responses of
macroeconomic variables to the two alternative sources of uncertainty shocks. We focus
on the responses of unemployment, inflation, and the nominal interest rate following an
uncertainty shock.
IV.1. Calibration. We calibrate the structural parameters to match several steady-state
observations. For those structural parameters that do not affect the model’s steady state,
we calibrate their values to be consistent with other empirical studies in the literature.
The structural parameters to be calibrated include β, the subjective discount factor; h,
the habit persistence parameter; χ, the disutility of working parameter; η, the elasticity of
substitution between differentiated retail products; α, the elasticity of matching with respect
to searching workers; µ, the matching efficiency parameter; ρ, the job separation rate; φ,
the flow of unemployment benefits (in final consumption units); κ, the fixed cost of posting
vacancies; b, the Nash bargaining weight; Ωp , the price adjustment cost parameter; π, the
steady-state inflation rate (which is also the inflation target); φπ , the Taylor-rule coefficient
for inflation; and φy , the Taylor-rule coefficient for output. In addition, we need to calibrate
the parameters in the shock processes. The calibrated values of the model parameters are
summarized in Table 1.
We set β = 0.99, so that the model implies a steady-state real interest rate of 4 percent
per year. As a baseline for our analysis, we set h = 0 so that there is no internal habit
formation. When we study the role of habit persistence in amplifying uncertainty shocks
relative to the baseline, we assume a value of h = 0.6, in line with empirical studies such as
Boldrin et al. (2001).
We calibrate the parameters related to labor search frictions based on existing studies. We
set the matching elasticity parameter α to 0.5 and the wage bargaining parameter b to 0.5
following Blanchard and Galı́ (2010). We calibrate the job separation rate ρ to 0.1, which is
consistent with an average monthly job separation rate of about 3.5 percent observed in the
Job Openings and Labor Turnover Survey (JOLTS). We follow Hall and Milgrom (2008) by
setting φ to 0.25, implying that the unemployment benefit is on average about 25 percent of
wage earnings.
We choose the value of the vacancy cost parameter κ so that, in the steady state, the
total cost of posting vacancies is about 2 percent of gross output. To assign a value of
κ then requires knowledge of the steady-state number of vacancies v and the steady-state
level of output Y . We calibrate the value of v such that the steady-state vacancy filling
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
16
rate is q v = 0.7, as in den Haan et al. (2000). We target a steady-state unemployment rate
of U = 0.064, to be consistent with the average unemployment rate in our data. Given
the steady-state value of the job separation rate ρ = 0.1, we obtain m = ρN = 0.0936.
Thus, we have v =
m
qv
=
0.0936
0.7
= 0.134. To obtain a value for Y , we use the aggregate
production function that Y = ZN and normalize the level of technology such that Z = 1.
This procedure yields a calibrated value of κ = 0.14.
Given the steady-state values of m, u, and v, we use the matching function to obtain an
average matching efficiency of µ = 0.645. To obtain a value for χ, we solve the steady-state
system so that χ is consistent with an unemployment rate of 6.4 percent. The process results
in χ = 0.547. We set the real wage rigidity parameter to γ = 0.8.
We also calibrate the parameters related to nominal rigidities following the literature. In
particular, we set η = 10 so that the average markup is about 11 percent, which lies in the
range estimated by Basu and Fernald (1997). We set the price adjustment cost parameter
to Ωp = 112 so that, to a first-order approximation, the slope of the Phillips curve in our
model corresponds to that implied by a Calvo model with a duration of price contracts of
four quarters.
For the Taylor rule parameters, we set φπ = 1.5 and φy = 0.2. We set π = 1.005, so
that the steady-state inflation rate is about 2 percent per year, corresponding to the Federal
Reserve’s inflation objective.
We calibrate the parameters in the first-moment technology shock following the RBC literature. In particular, we set the average standard deviation to σz = 0.01 and the persistence
parameter to ρz = 0.90.
We calibrate the parameters in the second-moment shock based on our VAR evidence.
As we have discussed in the empirical section, a one standard deviation shock to consumer
uncertainty raises the measure of uncertainty by 1.36 units relative to the sample mean of
3.47. Thus, the shock is equivalent to a 39.2 percent increase in the level of uncertainty
relative to its mean (1.36/3.47 = 0.392). Since we calibrate the mean standard deviation in
our model to 1 percent, we set the standard deviation of the uncertainty shock to σσz = 0.392
in light of the VAR evidence.
Our VAR evidence also shows that the effects of the uncertainty shock on measured uncertainty gradually decline over time. Specifically, in a period of 12 months, consumer uncertainty falls gradually to about 34 percent of its peak. This observation suggests that, if the
uncertainty shock is approximated by an AR(1) process—as we assume in the model—then
the persistence parameter should be about 0.913 at monthly frequencies (i.e., 0.91312 ≈ 0.34).
In our quarterly model, this implies a value of the persistence parameter of about ρσz = 0.76
(i.e., 0.9133 = 0.76).
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
17
IV.2. Macroeconomic effects of uncertainty shocks. To examine the dynamic effects
of uncertainty shocks, which are second-moment shocks in our model, we solve the model
using third-order approximations to the equilibrium conditions around the steady state. We
then compute the impulse responses following an uncertainty shock.9 We show that interactions between an option-value channel associated with search frictions and an aggregate
demand channel associated with nominal rigidities help amplify the macroeconomic effects
of uncertainty.
IV.2.1. The option-value channel. To illustrate the option-value channel, we focus on a
flexible-price version of the DSGE model, in which the aggregate demand channel is shut
off. The dynamic effects of uncertainty on labor market variables in our model can be best
understood by examining the responses of the match value (J F ) to uncertainty shocks. As
equation (20) shows, the match value depends on both the current-period profit and the
continuation value of a job match, with the latter discounted by the real interest rate. Uncertainty creates a precautionary saving motive which, all else equal, reduces the real interest
rate and raises employment, by raising the continuation value of a job match, and thus output. This expansionary effect of uncertainty is similar to that found in a standard RBC
model (e.g., Gilchrist and Williams (2005) and Basu and Bundick (2011)).
However, unlike the RBC model with a spot labor market, our model with search frictions
implies that a job match represents a long-term employment contract that is irreversible in
our model. As in the model of irreversible investment studied by Bernanke (1983), uncertainty gives rise to a real option-value effect that is contractionary. Facing higher uncertainty,
the option value of waiting increases and the expected value of a job match decreases, inducing firms to post fewer vacancies, making it harder for unemployed workers to find jobs,
and ultimately raising the equilibrium unemployment rate.
To illustrate the opposing effects from the option-value channel and precautionary savings,
we plot in Figure 5 the impulse responses of unemployment, consumption, the real interest
rate, and the match value following an uncertainty shock in the flexible-price model. The
figure shows that consumption and the real interest rate both decline following an increase in
9We
follow the same procedure as Fernández-Villaverdes et al. (2011) to compute the impulse responses.
In particular, we first simulate the model (with third-order approximations to the decision rules) for 2,096
periods, starting from the deterministic steady state and with all shocks hitting the economy. We discard the
first 2,000 periods to avoid dependence on initial conditions. We use the remaining 96 periods to compute
the ergodic mean of each variable. Starting from the ergodic means, we conduct two different simulations
of 20 periods each, one with an uncertainty shock (i.e., a one-standard-deviation increase in uncertainty in
the first period) and the other with no shocks. The impulse responses are then calculated as the percentage
differences between these two simulations. In reporting the impulse responses, we annualize the units of the
inflation rate and the nominal interest rate.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
18
uncertainty, indicating the presence of precautionary saving. However, with search frictions,
the option-value channel prevails over the precautionary saving effects, leading to an overall
recession with a lower match value and a higher unemployment rate. This option-value
channel would be absent in the standard RBC model with a spot labor market. Thus, our
results suggest that incorporating labor search frictions in the DSGE model helps explain
the macroeconomic effects of uncertainty.
IV.2.2. The aggregate demand channel. We now examine the aggregate demand channel for
the transmission of uncertainty shocks. For this purpose, we consider the benchmark model
with both search frictions and nominal rigidities.
Figure 6 displays the impulse responses of several key macroeconomic variables to a technology uncertainty shock. As shown in the figure, heightened uncertainty raises the unemployment rate by reducing aggregate demand. As demand falls, the relative price of
intermediate goods declines, reducing firms’ profit and the value of a job match. Firms
respond to the decline in the match value by posting fewer vacancies. With fewer vacancies
available, the job finding rate for searching workers declines and the unemployment rate
rises. As more workers are unemployed, household income falls, reinforcing the initial decline in aggregate demand and further amplifying the recessionary effects of uncertainty on
macroeconomic activity.
Since uncertainty depresses aggregate demand, it also lowers inflation. Under the Taylor
rule, the central bank lowers the nominal interest rate to alleviate the adverse effects of
uncertainty. Nonetheless, equilibrium unemployment still rises and equilibrium inflation still
falls following a rise in uncertainty. Thus, the theory’s predictions are broadly in line with
our empirical evidence that uncertainty shocks act like a negative aggregate demand shock.
The magnitude of the increase in unemployment following an uncertainty shock in the
DSGE model is somewhat smaller than, but comparable to, that estimated in the VAR
model. In particular, the VAR model implies that a one-standard-deviation increase in
consumer uncertainty (shown in Figure 2) leads to a peak increase of unemployment of
about 2.55 percent relative to the sample average (i.e., an increase in unemployment of
0.163 percentage points from a sample average of 6.4 percent). The DSGE model implies
an increase of unemployment of about 1.60 percent relative to the ergodic mean, so that
the DSGE model can generate about 63 percent of the observed increase in unemployment
following an uncertainty shock (1.60/2.55 ≈ 0.63).
IV.2.3. Interactions between the option-value channel and the aggregate demand channel. In
our model, search frictions and nominal rigidities have important interactions that amplify
the macroeconomic effects of uncertainty shocks. This is illustrated in Figure 7, which
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
19
displays the response of unemployment to a technology uncertainty shock in four different
models: the benchmark model (the solid blue line), a model with nominal rigidities but
with low search frictions (the dashed green line), a model with search frictions but with no
nominal rigidities (the dotted magenta line), and a model with habit formation (the dashed
and dotted red line).10
Figure 7 shows that the response of unemployment to uncertainty in the benchmark model
is much larger than those in the model with flexible prices and in the model with low search
frictions. In particular, the relatively small impact of uncertainty on real economic activity
in the model with low search frictions is in line with the findings of Born and Pfeifer (2014)
in a standard DSGE model without search frictions.11 This result suggests that the optionvalue channel associated with search frictions and the aggregate demand channel stemming
from nominal rigidities are both important for amplifying uncertainty shocks.
The figure also shows that incorporating habit persistence helps further amplify the effects
of uncertainty on unemployment. Habit formation increases the persistence of the negative
effects of uncertainty and thus induces a greater decline in the present value of a job match.
As a consequence, in the presence of habit formation, unemployment rises more sharply following the increase in uncertainty than in the benchmark model. In contrast, in a standard
model without search frictions, habit formation dampens the effect of uncertainty on economic activity, since the consumption decline is more muted in this case (see, for instance,
Born and Pfeifer (2014)). In our model, while more muted, the more persistent decline
in consumption under habit formation also amplifies the effect of the option-value channel,
resulting in a greater rise in the unemployment rate in response to an uncertainty shock.
Firms refrain from hiring since the possibility of a bad hiring decision may have long-lasting
negative consequences ex post.
Overall, incorporating habit formation brings the magnitude of the peak unemployment
response much closer to that estimated from the VAR model (2.38 percent vs. 2.55 percent).
These results are quantitatively similar even when we remove real wage rigidities (by setting
γ to 0 instead of 0.8), as shown in Figure 8.
10In
the model with low search frictions, we set the vacancy cost parameter to κ = 0.01 (instead of the
benchmark calibration of 0.14) and the job separation rate to δ = 0.9 (instead of the benchmark calibration
of 0.1). These parameters are chosen to approximate a model with frictionless (and spot) labor markets. In
the model with no nominal rigidities, we set the price adjustment cost parameter to Ωp = 0 and keep all the
other parameters at their calibrated values. In the model with habit formation, we set the habit persistence
parameter to h = 0.6 and keep all the other parameters at the calibrated values.
11
With low search frictions in our model, the magnitude of the output responses to a technology uncertainty shock (not reported) is comparable to that obtained by Born and Pfeifer (2014).
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
20
V. Conclusion
We study the macroeconomic effects of uncertainty shocks and find that uncertainty shocks
act like aggregate demand shocks both in the data and in a DSGE model with search frictions
and sticky prices.
Using novel measures of uncertainty from survey data and a VAR model, we document
robust evidence that an uncertainty shock leads to a rise in unemployment and declines
in inflation and the nominal interest rate. This result is robust to alternative measures of
uncertainty, alternative identification strategies, and alternative model specifications.
Incorporating search frictions in a DSGE model is important for understanding the macroeconomic effects of uncertainty. We show that search frictions give rise to an option-value
channel through which uncertainty can lead to a recession even in a flexible-price model.
Incorporating nominal rigidities opens up an aggregate-demand channel that reinforces the
option-value channel to generate quantitatively significant recessionary effects of uncertainty.
When the model is further augmented with habit formation, the calibrated DSGE model
generates an unemployment response to uncertainty with a size close to that estimated from
the VAR model. Thus, interactions between search frictions and nominal rigidities are important for amplifying the macroeconomic effects of uncertainty shocks.
To highlight the aggregate demand effects of uncertainty shocks, we have focused on a
stylized model that abstracts from some realistic and potentially important features of the
actual economy. For example, the model does not have endogenous capital accumulation and
is thus not designed to study the effects of uncertainty shocks on business investment. To the
extent that investment adjustments are costly, a similar option-value channel for investment
would operate so that increases in uncertainty would reduce investment expenditures. Thus,
incorporating endogenous capital accumulation in our model with search frictions may have
important implications for the quantitative magnitude of the responses of potential and
equilibrium output. However, in light of several recent studies in the literature (Basu and
Bundick, 2011; Fernández-Villaverde et al., 2013), incorporating capital accumulation in
a DSGE model with nominal rigidities is unlikely to change the qualitative transmission
mechanism of uncertainty shocks that we have identified in this paper.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
21
References
Arellano, C., Y. Bai, and P. Kehoe (2011): “Financial Markets and Fluctuations in
Uncertainty,” Federal Reserve Bank of Minneapolis Research Department Staff Report.
Auerbach, A. J. and Y. Gorodnichenko (2012): “Measuring the Output Responses
to Fiscal Policy,” American Economic Journal: Economic Policy, 4, 1–27.
Bachmann, R., S. Elstner, and E. R. Sims (2013): “Uncertainty and Economic Activity: Evidence from Business Survey Data,” American Economic Journal: Macroeconomics,
5, 217–249.
Bachmann, R. and G. Moscarini (2011): “Business Cycles and Endogenous Uncertainty,” Yale University Working Paper.
Baker, S. R., N. Bloom, and S. J. Davis (2011): “Measuring Economic Policy Uncertainty,” Stanford University Working Paper.
Basu, S. and B. Bundick (2011): “Uncertainty Shocks in a Model of Effective Demand,”
Boston College Working Paper.
Basu, S. and J. G. Fernald (1997): “Returns to Scale in U.S. Production: Estimates
and Implications,” Journal of Political Economy, 105, 249–283.
Bernanke, B. S. (1983): “Irreversibility, Uncertainty, and Cyclical Investment,” Quarterly
Journal of Economics, 98, 85–106.
Bidder, R. M. and M. E. Smith (2012): “Robust Animal Spirits,” Journal of Monetary
Economics, 59, 738–750.
Blanchard, O. J. and J. Galı́ (2010): “Labor Markets and Monetary Policy: A New
Keynesian Model with Unemployment,” American Economic Journal: Macroeconomics,
2, 1–30.
Bloom, N. (2009): “The Impact of Uncertainty Shocks,” Econometrica, 77, 623–685.
——— (2014): “Fluctuations in Uncertainty,” Journal of Economic Perspectives, forthcoming.
Bloom, N., M. Floetotto, N. Jaimovich, I. Saporta-Eksten, and S. J. Terry
(2012): “Really Uncertain Business Cycles,” NBER Working Paper 18245.
Boldrin, M., L. J. Christiano, and J. D. Fisher (2001): “Habit Persistence, Asset
Returns, and the Business Cycle,” American Economic Review, 91, 149–166.
Born, B. and J. Pfeifer (2014): “Policy Risk and the Business Cycle,” Journal of
Monetary Economics, 68, 68–85.
den Haan, W. J., G. Ramey, and J. Watson (2000): “Job Destruction and Propagation
of Shocks,” American Economic Review, 90, 482–498.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
22
Fernández-Villaverde, J., P. Guerrón-Quintana, K. Kuester, and J. RubioRamı́rez (2013): “Fiscal Volatility Shocks and Economic Activity,” University of Pennsylvania Working Paper.
Fernández-Villaverdes, J., P. Guerrón-Quintana, J. F. Rubio-Ramı́rez, and
M. Uribe (2011): “Risk Matters: The Real Effects of Volatility Shocks,” American
Economic Review, 101, 2530—2561.
Gertler, M. and P. Karadi (2013): “Monetary Policy Surprises, Credit Costs and
Economic Activity,” Unpublished manuscript, New York University and the European
Center Bank.
Gertler, M. and A. Trigari (2009): “Unemployment Fluctuations with Staggered Nash
Wage Bargaining,” Journal of Political Economy, 117.
Gilchrist, S., J. W. Sim, and E. Zakrajsek (2010): “Uncertainty, Financial Frictions,
and Investment Dynamics,” Boston University Working Paper.
Gilchrist, S. and J. C. Williams (2005): “Investment, Capacity, and Uncertainty: A
Putty-Clay Approach,” Review of Economic Dynamics, 8, 1–27.
Gourio, F. (2012): “Disaster Risk and Business Cycles,” American Economic Review, 102,
2734–2766.
Hall, R. E. (2005): “Employment Fluctuations with Equilibrium Wage Stickiness,” American Economic Review, 95, 50–65.
Hall, R. E. and P. R. Milgrom (2008): “The Limited Influence of Unemployment on
the Wage Bargain,” American Economic Review, 98,.
Ilut, C. L. and M. Schneider (2011): “Ambiguous Business Cycles,” Working paper,
Duke University and Stanford University.
Jurado, K., S. C. Ludvigson, and S. Ng (2013): “Measuring Uncertainty,” Unpublished manuscript, Columbia University and New York University.
Leduc, S. and K. Sill (forthcoming): “Expectations and Economic Fluctuations: An
Analysis Using Survey Data,” Review of Economics and Statistics.
Leduc, S., K. Sill, and T. Stark (2007): “Self-Fulfilling Expectations and the Inflation
of the 1970s: Evidence from the Livingston Survey,” Journal of Monetary Economics, 54,
433–459.
Rotemberg, J. J. (1982): “Sticky Prices in the United States,” Journal of Political Economy, 90, 1187–1211.
Schaal, E. (2012): “Uncertainty, Productivity and Unemployment in the Great Recession,”
Unpublished manuscript, Federal Reserve Bank of Minneapolis.
Scotti, C. (2012): “Surprise and Uncertainty Indexes: Real-time Aggregation of RealActivity Macro Surprises,” Unpublished manuscript, Federal Reserve Board.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
23
Sims, C. A. and T. Zha (1998): “Bayesian Methods for Dynamic Multivariate Models,”
International Economic Review, 39, 949–968.
Swanson, E. T. and J. C. Williams (2014): “Measuring the Effect of the Zero Lower
Bound on Medium- and Longer-Term Interest Rates,” American Economic Review (forthcoming).
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
24
Table 1. Benchmark parameter calibration
Parameter Description
value
Structural parameters
β
Household’s discount factor
0.99
χ
Scale of disutility of working
0.547
h
Habit persistence
0
η
Elasticity of substitution between differentiated goods 10
α
Share parameter in matching function
0.50
µ
Matching efficiency
0.645
ρ
Job separation rate
0.10
φ
Flow benefit of unemployment
0.25
κ
Flow cost of vacancy
0.14
b
Nash bargaining weight
0.5
γ
Real wage rigidity
0.8
Ωp
Price adjustment cost
112
π
Steady-state inflation (or inflation target)
1.005
φπ
Taylor-rule coefficient for inflation
1.5
φy
Taylor-rule coefficient for output
0.2
Shock parameters
ρz
Persistence of technology level shock
0.90
σz
Mean volatility of technology shock
0.01
ρσz
Persistence of technology uncertainty shock
0.76
σσz
Standard deviation of technology uncertainty shock
0.392
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
Consumer Uncertainty vs. VIX Index
Index
70
25
Percent
15
Consumer
uncertainty
(right axis)
60
50
10
VIX Index
(left axis)
40
30
5
20
10
0
1978
0
1983
1988
1993
1998
2003
Source: Thomson Reuters/University of Michigan Survey of Consumers, Bloomberg
2008
2013
Figure 1. Consumers’ perceived uncertainty from the Michigan Survey of
Consumers in the United States (solid line) versus the VIX/VXO index from
the Chicago Board of Exchange (dashed line). The grey shaded areas indicate
NBER recession dates in the United States. Data frequencies are monthly.
The consumer uncertainty series starts in January 1978 and the VIX/VXO
series starts in January 1986. Both series end by October 2013. Three-month
moving averages are plotted.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
26
.90 Error Bands
Uncertainty
1.4439
Unemployment
0.2565
0
0
−0.5338
−0.2881
Inflation
0.2853
0
−0.5748
Interest rate
0.3327
0
−0.5157
12
24
36
48
Figure 2. The effects of a one-standard deviation shock to perceived uncertainty in the Michigan Survey of Consumers: uncertainty measure ordered
first. The solid lines represent median responses of the variables to a onestandard-deviation increase in the innovations to uncertainty. The dashed
lines around each solid line represent the 90-percent error bands for the estimated median impulse responses. The units of the horizontal axes are months.
The units of the vertical axes are percentage points.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
27
.90 Error Bands
Unemployment
Inflation
0.2995
0.1711
0
0
−0.2672
−0.5651
Interest rate
Uncertainty
0.1745
1.4312
0
0
−0.5212
−0.3377
12
24
36
48
Figure 3. The effects of a one-standard deviation shock to perceived uncertainty in the Michigan Survey of Consumers: uncertainty measure ordered last.
The solid lines represent median responses of the variables to a one-standarddeviation increase in the innovations to uncertainty. The dashed lines around
each solid line represent the 90-percent error bands for the estimated median
impulse responses. The units of the horizontal axes are months. The units of
the vertical axes are percentage points.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
28
.90 Error Bands
Uncertainty
4.0884
Unemployment
0.2095
0
0
−1.5555
−0.3165
Inflation
0.1761
Interest rate
0.3181
0
0
−0.4595
−0.5355
12
24
36
48
Figure 4. The effects of a one-standard deviation shock to the VIX. The solid
lines represent median responses of the variables to a one-standard-deviation
increase in the innovations to uncertainty. The dashed lines around each solid
line represent the 90-percent error bands for the estimated median impulse
responses. The units of the horizontal axes are months. The units of the
vertical axes are percentage points.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
Unemployment
Percent deviations
0.1
0
0.08
-1
0.06
-2
0.04
-3
0.02
-4
0
Consumption
-5
0
0
#10 -3
29
5
#10 -3
10
15
20
Real interest rate
0
5
-0.01
-2
-0.02
-3
-0.03
-4
-0.04
-5
15
20
15
20
Match value
0
-1
10
-0.05
0
5
10
15
20
0
5
10
Figure 5. Impulse responses to a technology uncertainty shock in the DSGE
model with flexible prices.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
Percent deviations
Unemployment
30
Inflation
2
0
1.5
-0.05
1
-0.1
0.5
0
-0.15
0
5
10
15
20
0
5
Nominal interest rate
10
15
20
15
20
Vacancies
0
1
0
-0.1
-1
-0.2
-2
-0.3
-3
0
5
10
15
20
0
Match value
5
10
Labor market tightness
0.5
1
0
0
-0.5
-1
-1
-2
-1.5
-3
0
5
10
15
20
0
5
10
15
20
Quarters
Figure 6. Impulse responses of macroeconomic variables to a technology
uncertainty shock in the DSGE model with sticky prices.
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
31
Unemployment responses to uncertainty shock
2.5
Habit
Benchmark
Low search frictions
Flex price
Percentage deviations
2
1.5
1
0.5
0
-0.5
0
2
4
6
8
10
12
14
16
18
Quarters after shock
Figure 7. Amplification mechanisms for technology uncertainty.
20
UNCERTAINTY SHOCKS ARE AGGREGATE DEMAND SHOCKS
32
Unemployment responses to uncertainty shock
2.5
Habit
Benchmark
Low search frictions
Flex price
Percentage deviations
2
1.5
1
0.5
0
0
2
4
6
8
10
12
14
16
18
20
Quarters after shock
Figure 8. Amplification mechanisms for technology uncertainty in the model
with no real wage rigidities.